YOLOv5
YOLOv5 is an open-source computer vision model developed by Ultralytics, implemented in PyTorch, designed for object detection, instance segmentation, and image classification. It processes various input types including URLs, filenames, and image arrays, and outputs detection results in formats such as torch tensors, pandas dataframes, or JSON. The model supports exporting to deployment formats like ONNX, CoreML, and TFLite. YOLOv5 includes multiple model variants, such as the lightweight YOLOv5n (Nano) and instance segmentation models up to version 7.0. It integrates with PyTorch Hub for easy inference and supports datasets in COCO format as well as integrations with platforms like Roboflow and AWS.
YOLOv5 is a PyTorch-based open-source model for real-time object detection, segmentation, and classification.
Real-Time Object Detection
Detect and localize objects in images or video streams for applications such as surveillance or autonomous systems.
Instance Segmentation
Outline precise boundaries of objects within images for detailed analysis in medical imaging or robotics.
Image Classification
Label entire images to categorize content for sorting or filtering in large datasets.
pip install -U ultralytics ensuring Python >=3.8 and PyTorch >=1.7 are installed.git clone https://github.com/ultralytics/yolov5 and install dependencies using pip install -r requirements.txt.model = torch.hub.load('ultralytics/yolov5', 'yolov5s').results = model('image.jpg').yolov5 train --data data.yaml --weights yolov5s.pt.